Random tateti

random_tateti_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Funciones para manipular el tablero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice\n",
    "\n",
    "def random_move(board, who):\n",
    "    i, j = choice(get_available(board))\n",
    "    board[i][j] = who\n",
    "    \n",
    "def _get_diags(board):\n",
    "    return [board[0][0], board[1][1], board[2][2]], [board[0][2], board[1][1], board[2][0]]\n",
    "\n",
    "def player_won(board, who):\n",
    "    target_row = [who] * 3\n",
    "    return (\n",
    "        any(row==target_row for row in board) or # filas \n",
    "        any(list(row)==target_row for row in zip(*board)) or # columnas\n",
    "        any(row==target_row for row in _get_diags(board)) # diagonales\n",
    "    )\n",
    "\n",
    "empty = '-'\n",
    "def empty_board():\n",
    "    return [[empty] * 3 for _ in range(3)]\n",
    "\n",
    "def get_available(board):\n",
    "    return [(i, j) for i in range(3) for j in range(3) if board[i][j] is empty]\n",
    "\n",
    "def is_finished(board):\n",
    "    return not any(e is empty for row in board for e in row)\n",
    "\n",
    "def print_board(board):\n",
    "    [print(row) for row in board]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Funcion principal para simular una jugada random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import cycle\n",
    "\n",
    "def play():\n",
    "    board = empty_board()\n",
    "    player_iter = cycle('XO')\n",
    "    winner = None\n",
    "    moves = {'X': 0, 'O': 0}\n",
    "    while not is_finished(board):\n",
    "        who = next(player_iter)\n",
    "        moves[who] += 1\n",
    "        random_move(board, who)\n",
    "        if player_won(board, who): \n",
    "            winner = who\n",
    "            break\n",
    "    return board, winner, moves.get(winner)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X 5\n",
      "['O', 'O', 'X']\n",
      "['X', 'X', 'X']\n",
      "['O', 'O', 'X']\n"
     ]
    }
   ],
   "source": [
    "board, winner, moves = play()\n",
    "\n",
    "print(winner, moves)\n",
    "print_board(board)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experimentos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def winner_experiment(n=1000):\n",
    "    ends = {'X': 0, 'O': 0, None: 0}\n",
    "    for _ in range(n):\n",
    "        _, winner, moves = play()\n",
    "        ends[winner] += 1\n",
    "    return {k: v/n for k, v in ends.items()}\n",
    "\n",
    "def n_X_moves_experiment(n=1000):\n",
    "    l = []\n",
    "    for _ in range(n):\n",
    "        _, winner, moves = play()\n",
    "        if winner != 'X': continue\n",
    "        l.append(moves)\n",
    "    return {'moves': sum(l) / len(l)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of moves for X to win"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bc9fb50>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.DataFrame([n_X_moves_experiment() for _ in range(100)]).boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prob of winning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11be4ac40>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame([winner_experiment() for _ in range(100)]).boxplot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}